# Roto Fantasy Baseball Part 3: A Can't Miss Mathematical Approach to Closers

March 8, 2012In parts 1 and 2 of this three-part series, we discussed the mathematical evaluation of hitters and starting pitchers for fantasy baseball using a formula we called Dominance Factor (DF).

If you missed those articles you can catch up on the series here:

Now it is time to dive into the trickiest position to project in fantasy baseball, the relief pitcher.

Easily 70% of a reliever’s value in standard scoring is tied directly to saves, as you will see in our formula. Also, we all know that saves are without a doubt an opportunity statistic. Whether a pitcher is a closer or lost in middle relief can change on the mere whim of a manager or general manager. The Padres closer who carried your team to the top of the saves category, can suddenly become irrelevant when the Yankees decide that the player would make a great set up man to chase down Boston.

Because of this, I’m going to start this article with a MAJOR disclaimer.

The closing situation for one out of every three major league teams is virtually impossible to predict. Coupling this to the facts that the stat is highly opportunistic and waiver wire closers are common, I personally avoid many closers in early rounds and feel one closer is about as good as another in the fantasy world.

However, my thoughts on where closers should be drafted are not the focus of this article. What we want to do is to mathematically evaluate relief pitchers based on whatever tentative projections we may have so you, the fantasy owner, can be in the best possible situation when the inevitable “run” on closers happens..

Once again, we’ll be using a DF formula to compare players based on projected stats. In addition to the realization that saves make or break relievers in fantasy, the second key point to understand is that players become a closer for a reason. Successful closers typically keep their jobs for 2 reasons, they keep men off base (low whip) and they can pitch out of trouble when they get in a jam (high K/9).

You can make arguments that ERA matters. If a closer gives up two runs, often times the game is over, meaning ERA doesn't tell you much.

Maybe total strikeouts help your team’s bottom line? I would argue that the difference between Jonathon Papelbon and Sergio Santos projects to be 1 strikeout, in Santos's favor!

At the end of the day closers who lose their jobs do so by letting too many men of base or by relying on contact for outs. Because of this, we are going to use a grand total of three statistics to analyze relievers which are broken out as follows for DF:

Saves-70 points, WHIP-15 points, K/9-15 points

If you are familiar with the series, you know we need our two favorite imaginary players to calculate DF. The first player is the fantasy stud. He projects to lead the league in just about everything we’ve decided to care about for relievers. Craig Kimbrel did this and then some last season.

The stud's stat line is: 40 Saves 0.90 WHIP 13 K/9

The second player is the fantasy bum. This player has a stat line similar to what happens when a team has to use a career middle reliever to close out games or uses the dreaded "closer by committee." These guys typically lose a ton of games along with their job as a 9th inning specialist (anyone remember J.P. Howell?).

His stat line is: 15 Saves 1.30 WHIP 7 K/9

Ideally, reliable statistics would exist for relief pitchers. Unfortunately they do not. So we’re just going to have to do the best we can with what we have.

Once you have exported the projections to a spreadsheet, it’s time to build one last formula before our draft. Just like with the hitters, we are going to be evaluating on the percentage of the way from the bum level to the stud level. For stats where more is better (K/9, Saves), we’ll subtract the bum’s stat from each player’s stat and divide by the stud’s stat minus the bum’s stat. This number is then multiplied times the points for that category. If lower numbers are better (WHIP), we’ll do the opposite and subtract each player’s stat from the bum’s stat and divide by the bum’s stat minus the stud’s stat.

I’m certain you can stay with me one more time.

A good example to use here would be John Axford.

Assume Axford to project to a stat line of: 38 Saves, 1.18 WHIP, 10.4 K/9

As with most players, he falls some where between the stud and the bum. First looking at Saves as an example we get:

(38-15)/(40-15)x70 = 64.4 out of 70 Points

If we look at WHIP, where less is better, the calculation would be:

(1.3-1.18)/(1.3-0.90)*15 = 4.5 out of 15 Points

Lastly looking at K/9, we have:

(10.4-7)/(13-7)*15 = 8.5 out of 15 Points

Adding all of these together, Axford ends up with a DF of 77.4, good for 4th overall for relievers behind Craig Kimbrel (86.2), Jonathan Papelbon (83.1), and Carlos Marmol (80.3).

While it may seem complicated, if you use Excel and place W, ERA, SV, K, WHIP and K/9 in the C through H columns and paste the below formula in column I you will have the DF for every single player in seconds.

Excel formula for DF for Column I:

=((E2-15)/25)*70++((1.3-G2)/0.4)*15+((H2-7)/6)*15

An important thing to note is that 19 of the 30 major league closers project to have a DF between 50 and 70. When you throw in the save variability based on a number of factors, 2/3 of the league’s closers are virtually the same player. Remember this when you start to pull the trigger on Brian Wilson 5 rounds ahead of Brandon League.

All together you now have 3 simple formulas to evaluate every player in major league baseball for this upcoming draft. Armed with this data, you will no doubt be the most prepared and most unbiased owner in your league come draft day. Now just manage your team the right way and find a nice shelf for your soon to be won trophy.

Have a great season!

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